การประมวลผลภาพสำหรับการจำแนกรูปภาพฟันดัสโดยใช้การเรียนรู้เชิงลึก
Article Sidebar
Main Article Content
บทคัดย่อ
งานวิจัยนี้เสนอการใช้คอมพิวเตอร์เพื่อจำแนกระดับความรุนแรงเบาหวานเข้าจอประสาทตา 4 ระดับคือ ระดับปกติ ระดับขั้นเบา ระดับขั้นปานกลางและระดับขั้นรุนแรงจากภาพสีฟันดัสโดยใช้การประมวลผลภาพด้วยการเรียนรู้เชิงลึก (Deep Learning) การพัฒนาแบบจำลองสำหรับจำแนกรูปภาพฟันดัสแสดงให้เห็นว่าการสร้างแบบจำลองของงานวิจัยนี้ มีความแม่นยำมากกว่างานวิจัยก่อนหน้านี้ที่ใช้วิธี Machine Learning นอกจากนี้ งานวิจัยนี้ได้นำแบบจำลองที่ได้ไปพัฒนาเป็นระบบต้นแบบ แสดงให้เห็นว่ามีความแม่นยำในการจำแนกระดับความรุนแรงของโรคเบาหวานเข้าจอประสาทตาสามารถช่วยให้จักษุแพทย์วิเคราะห์ระดับความรุนแรงของเบาหวานเข้าจอประสาทตาจากภาพสีฟันดัสได้อย่างมีประสิทธิภาพ
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
I/we certify that I/we have participated sufficiently in the intellectual content, conception and design of this work or the analysis and interpretation of the data (when applicable), as well as the writing of the manuscript, to take public responsibility for it and have agreed to have my/our name listed as a contributor. I/we believe the manuscript represents valid work. Neither this manuscript nor one with substantially similar content under my/our authorship has been published or is being considered for publication elsewhere, except as described in the covering letter. I/we certify that all the data collected during the study is presented in this manuscript and no data from the study has been or will be published separately. I/we attest that, if requested by the editors, I/we will provide the data/information or will cooperate fully in obtaining and providing the data/information on which the manuscript is based, for examination by the editors or their assignees. Financial interests, direct or indirect, that exist or may be perceived to exist for individual contributors in connection with the content of this paper have been disclosed in the cover letter. Sources of outside support of the project are named in the cover letter.
I/We hereby transfer(s), assign(s), or otherwise convey(s) all copyright ownership, including any and all rights incidental thereto, exclusively to the Journal, in the event that such work is published by the Journal. The Journal shall own the work, including 1) copyright; 2) the right to grant permission to republish the article in whole or in part, with or without fee; 3) the right to produce preprints or reprints and translate into languages other than English for sale or free distribution; and 4) the right to republish the work in a collection of articles in any other mechanical or electronic format.
We give the rights to the corresponding author to make necessary changes as per the request of the journal, do the rest of the correspondence on our behalf and he/she will act as the guarantor for the manuscript on our behalf.
All persons who have made substantial contributions to the work reported in the manuscript, but who are not contributors, are named in the Acknowledgment and have given me/us their written permission to be named. If I/we do not include an Acknowledgment that means I/we have not received substantial contributions from non-contributors and no contributor has been omitted.
เอกสารอ้างอิง
D. Complications. (2019, Cited 1 October 2019). Diabetic Retinopathy. Available: http://www.diabetes .co.uk/diabetes-complications/diabetic-retinopathy.html [Accessed: July 20, 2020].
M. Bishop, "Pattern Recognition and Machine Learning," Springer, pp. 1–758, 2006.
L. Deng and D. Yu, "Deep Learning: Methods and Applications," Foundations and Trends in Signal Processing, pp. 1–199, 2014.
R. Keys, "Cubic convolution interpolation for digital image processing," IEEE Transactions on Acoustics, Speech, and Signal Processing, pp. 1153 - 1160, 1981.
R. Hirsch, "Exploring Colour Photography: A Complete Guide," Laurence King Publishing, pp. 1–360, 2004.
S. Johnson, "Stephen Johnson on Digital Photography," O'Reilly, pp. 1–305, 2006.
N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 886–893, 2005.
J. Quinlan, "C4.5: Programs for Machine Learning," ed: Morgan Kaufmann Publishers, 1993.
T. Daniel, "Discovering Knowledge in Data," A JOHN WILEY & SONS, 2005.
K. Crammer and Y. Singer, "On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines," Journal of Machine Learning Research, pp. 265–292, 2001.
M. Hassoun, "Fundamentals of Artificial Neural Networks (MIT Press)," A Bradford Book, 2003.
E. Humphrey and J. Bello, "Rethinking Automatic Chord Recognition with Convolution Neural Networks," in Proceedings of 11th International Conference on Machine Learning and Application, 2012.
B. Tilmann, "The Business Impact of Predictive Analytics," ed: IGI Global, pp. 118–119, ISBN 978-1-59904-252-7, 2007.
R. Mahmood and B. Ghimire, "Automatic detection and classification of Alzheimer' s Disease from MRI scans using principal component analysis and artificial neural networks," in Proceedings of the 20th International Conference on Systems Signals and Image Processing, pp. 133-137, 2013.
S. Sarraf and G. Tofighi, "Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data," in Proceedings of Future Technologies Conference, San Francisco, CA, USA, pp. 816-820, 2016.
Y. Han and M. Zhao, "A hybrid sequential feature selection approach for the diagnosis of Alzheimer's Disease," in Proceedings of International Joint Conference on Neural Networks, pp. 1216-1220, 2016.
Y. Jindaluang, "Risk Factors for Diabetic Retinopathy in Diabetic Patients, Tak Municipal Area," Buddhachinaraj Medical Journal, pp. 53-61, 2009.
A. Paisal and T. Kasetkasem, "Separation the mingling varieties of the mungbean seeds by image processing," Khon Kaen Agriculture Journal, pp. 240-247, 2011.
T. Tathawee, S. Prasarnpun, S. Onbua, T. Pinthong, and A. Suwannakom, "Orchid identification based on computer vision analysis," in Proceedings of The 6th National Science Research Conference, Thailand, pp. 47 - 56, 2014.
D. Muntham and L. Ingsrisawang, "An Application of Decision Tree Algorithms for Diagnosis of the Respiratory System: A Case Study of Pranakorn Sri Ayudthaya Hospital," Journal of Health Systems Research, Vol. 4, pp. 73-81, 2010.
S. Phimphisan, "Application of Data Mining for Diabetic Retinopathy Using Decision Tree," in Journal of Srivanalai Vijai, 2016.
A. Nain, S. Paul, and M. Maynard-Reid. (13 May 2020). Keras. Available: https://keras.io/ [Accessed: July 20, 2020].
T. Team. (13 May 2020). TensorFlow. Available: https://www.tensorflow.org/ [Accessed: July 20, 2020].
R. Kohavi, "A study of crossvalidation and bootstrap for accuracy estimation and model selection," in Proceedings of the Fourteenth International joint conference on Artificial Intelligence, Montreal, Canada, pp. 1137-1143, 1995.
